Future of Digital Surgery, with Senior Leaders from GE HealthCare, Intuitive, Mayo, SANTE, & Veranex | LSI USA '24

This panel highlights some speculations and trends from seasoned industry veterans to give us insights into the future of digital surgery.
Speakers
Lisa Carmel
Lisa Carmel
, Veranex
Janani Reisenauer
Janani Reisenauer
, Mayo Clinic
Philip Rackliffe
Philip Rackliffe
, GE Healthcare
Brian Miller
Brian Miller
, Intuitive
Dennis McWilliams
Dennis McWilliams
, Sante Ventures

Lisa Carmel  0:03  
Well, at least you know, we can find our headshot. Yeah.

You can tell we're having a little too much fun getting ready for this panel. We are very excited to. I'm very excited to moderate this panel, because I've got some amazing people here some rock star panelists, and we're going to definitely dissect and discuss the future of surgery. First, we'll we thought we'd go and everyone introduce themselves, tell them a little bit about their role. My name is Lisa Carmel, I lead strategic partnerships at Veranex. And in the event, you are not familiar with Veranex, we're a full service med tech only innovation solutions firm, purpose built, P backed. Thank you.

Brian Miller  0:57  
So my name is Brian Miller. And I was an engineer by training. And but I've been fortunate to be in the surgical robotics field for the past 24 years, started to computer motion for those of you that know the surgical robotics story. And 2003. Intuitive acquired us. So I've been fortunate to wear many hats over the last 24 years with early product development and research. So kind of, you know, the eight to 10 years looking out of the fun stuff, to advance development to kind of hardcore product development, getting things out to the market. I currently run all of our digital portfolio. And so we'll talk a bit about that today. But we focus on improving outcomes. beyond what the robotic system has done. You can think about the first 25 years was around providing additional dexterity for surgeons 3d vision, scaling of motions, like the next 25 will be that cognitive aspect that the data and AI type of technology will help so looking forward to the panel.

Janani Reisenauer  2:00  
Hi, everyone. I'm Janani Reisenauer. Our I'm a thoracic surgeon and interventional pulmonologist at Mayo Clinic in Rochester, Minnesota. My other hat that I wear is also overseeing innovation for the Department of Surgery at Mayo Clinic. As many industry who may or may not know we were recently approved to build a new hospital called Bold forward, unbound. And so this conversation fits perfectly in that landscape as we try to identify what are the key priorities that are going to go into our hospital of the future. It's a pleasure to be with you all today. Thank you.

Phil Rackliffe  2:37  
Thank you Philip    Rackliffe. I spent about 25, almost 30 years in healthcare and medtech kind of an interesting career, spent 20 at large companies then spent the previous six years ish raising capital, doing startups had a lot of fun being on the other side, here at this conference, which is excellent. In the last 18 months went back to a large multinational. So I joined GE HealthCare. I lead up globally, the image guided therapies business, that includes kind of three verticals, a lot of fluoroscopy, both fixed and mobile plus and electrophysiology, invasive cardiology business, you know, really excited to be here today, I think GE is on a very exciting track and where they think about going in the future. We just went as a standalone company about a year ago. And just in that one year, we've made somewhere north of 10 different equity investments to full scale purchase acquisitions, and have over 60 ai approved FDA approvals at this point. So a lot of the discussion today is going to be around how do we enhance the surgical practice through AI and other means so that we can improve patient outcomes.

Dennis McWilliams  3:53  
And I'm Dennis    McWilliams, I'm a partner at Sante Ventures. We're early stage Life Science investors, we invest across healthcare. So we invest in what I call hardcore medical devices, you know, biotech and therapeutics, and also have a pretty robust health tech and healthcare services practice. You know, my personal story actually started as an entrepreneur, most of my life has been working in small startups and you know, ranging from biotech to the surgery space. So I spent 25 years doing that, and then flipped over to, as we say, The Dark Side of venture capital, even though I think someday we're not dark. We're early stage investors. I mean, we're, we're probably crazy. But we do we, but we've made several investments. We've had a long standing thesis in Sante for over four years and investing in the digitization of the surgical LR it's been a big focus for us. We've made three investments actually one with with Brian Seaman intuitive, and it's been a great space and we're super excited about it.

Lisa Carmel  4:53  
Great, well, so future of surgery, digital surgery is a big one. So we thought we might have First we'll discuss and hone in on some of the issues, unmet needs, or hurdles. And we're going to sort of focusing on data. And with that in mind, maybe Dr. Rice an hour if you want to take charge here. Yeah,

Janani Reisenauer  5:15  
that's a great and boundless topic, which could extend into Thursday, if we let it. You know, we've we've been acquiring data for many, many, many, many years. The challenge has always been how do you interpret that? And what do you do with that, and in the surgical practice, much of the intra operative data is guided by preoperative data, which will then drive postoperative outcomes. So it really is not just fixed on that silo of the operating room, it really is from the second the patient hits the door to the patient goes home, and even in the modern era beyond what happens when that patient goes home as we start talking about home hospital and remote monitoring. So the obvious challenges are, what do you do with these terabytes and terabytes of data? How do you weed out the white noise? How do you identify a true unmet clinical need? And use the data to solve an unmet need, as opposed to just throwing data at something and then trying to identify a clinical need? How do you identify the consumer? Is it the provider? Is it the hospital? Or is it the patient? Who owns it? Who can who should be allowed to make decisions based off of it? And who's ultimately medically liable for that data? And the way I envision it is, is it's a table with multiple seats around the table. And here are all the key stakeholders, and here are all the key players. And it's just figuring out how those pieces align and work together.

Dennis McWilliams  6:52  
I mean, we kind of came at this, our investment thesis kind of evolved out of the changes you've talked about in the operating room. I mean, you know, we initially were like, Okay, well, what's the future of surgery? It's a very, it's, you know, half of all healthcare spend. And, you know, obviously, a huge theme is robotics. And so everybody's interested in robotics, sante, we've just decided we're too small of a venture fund to effectively play and make investments in the space. And so we're like, Well, what does that enable, if you think about, you know, robotics, and you know, all the digital equipment in the operating room, and what that's not providing, from a data standpoint, we're like, wow, we're barely even scratching the surface on that. And so I think it's been a key enablement, there's been several years of trying to figure that out. And we'll talk a little bit unsure about value proposition. And you know, what really drives that. But it is an unprecedented time across all of healthcare. But I think in particular, the surgical operating room has just had a dramatic shift in the amount of data that's available from these procedures as we turn these surgical operating rooms to be more technically enabled.

Brian Miller  7:49  
Yeah, just adding from kind of a med device perspective, you know, Dr. Reisner talked about the amount of data and really trying to understand, you know, what, what is important and what's not. And so I think, that is one of the huge aspects, it's about what is important in the data, but also is the data Correct? You know, when you start to look at technologies, like machine learning, you know, if you train it with bad data, you're gonna get a bad result. And so, but I think, you know, we'll get to the opportunities in a moment. But if you look across the ability to connect the data across the patient continuum, I think it's powerful. And, and so when you're able to really understand every step of the way, I think robotics gives you a great sensor in the operating room. That's one of the one of the benefits of that. And so, so I think there's huge opportunity, but I agree with Dr. Eisenhower, he had also can be overwhelming on exactly what you're gonna get from it.

Phil Rackliffe  8:38  
I think from a GE perspective, you know, there's billions upon billions of amounts of data that we collect, whether that be kind of preoperative diagnosis with CT and Mr. But we're we're focused on now is really on the intra operative data, there's so much that can be gleaned from a CB CT or a cone beam spin that can be used to help decrease procedure times decrease fluro and improve outcomes. Just by harnessing that data and being able to identify exactly where that lesion is or what the margins are. There's so much more that we can do with the data that we have. And that's what we're really focused on our investments over the past, really the last six months have been squarely in that space and a lot of our r&d product development moving forward, is there really intra operative improvement of surgical procedures.

Janani Reisenauer  9:32  
The one thing I'll layer on top of that is that data and AI are oftentimes thrown in together as a consolidated buzzword, but they really are two independent entities because I think that AI does not drive change management, but data does. And I think that that's a really important as we're talking about unmet needs on the landscape. I think that's a really important distinction to keep in mind.

Lisa Carmel  9:55  
Well, you know, we, we, we've talked, you know about some With the issues that we're seeing the hurdles, what about do you want to move on into the opportunities? And that you're seeing some of the upside on all of this?

Dennis McWilliams  10:13  
We haven't had a return in the space yet. But yes, we, we think we made some investments. I mean, you know, back to, it's funny, you'll feel your comment about your focus on interrupted data, we kind of broke our thesis similarly, down to source of data, I think that's been important for us, because certain data streams have different costs associated with them, you know, interrupted data can be some of the most expensive data to collect, especially if you don't own the hardware, you're trying to collect it, you know, you mentioned, you know, typical one hour surgical procedure can produce five terabytes of data, if you can include the video, the room sounds, you know, the anesthesia equipment, I mean, it's a tremendous amount of data, we actually took our thesis from, well, there's actually really inexpensive data, you can get perioperatively. So before the operating procedure that can drive and improve surgical outcomes. That was Keila health, which we did with intuitive, and so really low cost data to, to bring in integrating some slightly more expensive data from the robot. And that's been a really interesting one to watch. I bracket that on the other side with a recent investment we made about nine months ago, called surgical safety technologies with the or black box and like, they're the exact opposite. They're collecting all five terabytes of data, processing all of it real time, super expensive computationally to do. And so I think they're just opportunities that range all across that. You really the question is, what's the value proposition to hospitals wanting to pay for? Because at the end of the day, who cares? If it's a lot of data? Who cares? If it's AI? To some extent, like, yes, you can improve patient outcomes, but the hospitals have got to choose to adopt it and pay for it. And that that's been a trick for a lot of these companies.

Lisa Carmel  11:53  
Well, Phil, do you want to talk about some of the upside on AI that you're Yeah,

Phil Rackliffe  11:59  
I think I think overall, like, like I said, we've made a huge push into the AI space, really over the past 12 to 15 months since we've been a standalone company. And GE HealthCare now kind of runs at its own beat, which is super fun and fun to kind of develop a culture around that. But we have hundreds of people now, and a very focused on AI and software solutions really, on the whole scope of the patient journey. But in particular, when we talk about IGT, I think about a product product we just launched called mo assist AI. And basically it's it's a way with no click to segment all of your vasculature, let's say you're going to do a prostate artery embolization, and be able to predict where to drop the embolic. Thus, to have the maximum impact on decreasing that the prostate size in this example, and not having downstream implications of going into the wrong vessel. But in doing that, you know, we're gonna predict we're gonna announce sorry, not a plug, but at SR next week sided interventional radiology, we're gonna show incredible data with like, 50%, less fluoro time 30% Less procedure time, by using this AI software, which is something we harness and generate, and then be able to segment it to enable, you know, clinicians to make better outcomes in the future.

Brian Miller  13:20  
I think for for intuitive, we look at two primary benefits for data and then analyzing that with with machine learning, but really, as a means to get to what a recommendation is. So the first is, you know, we you kind of look at surgeons like elite athletes, and they're wanting to continue to drive, you know, to the top of their performance. So the ability to go in and say, we can capture what happened during the procedure, so that you can go back, you can review, you can understand exactly what was done and potentially what you might do differently. We have several studies that demonstrate as you show somebody on their learning curve, so new robotic surgeon, as they're coming up their learning curve, you can actually look at how they interact with the technology. And you can distinguish between whether they are novices or experts, which is pretty powerful, because not not just because you can tell, but then you can go in and provide very focused recommendations. And and so I think there's that aspect that we're already seeing an impact and ability to really get towards this personalized and purposeful training. When you fast forward. Now, this will be a journey, but you start to say, okay, when you start to see millions and millions of procedures going by, you know exactly what technique a surgeon use, you know what technology was used during the procedure, you can then start to understand what the best approach would be for that next patient. And so when a patient presents themselves with a condition, you can go in and start to match it and say, this is probably the best pathway to generate the best results and so, so that will take time, there'll be a journey, but I do believe that there is an opportunity to provide that level of predictiveness So that will really start to reduce the variability in outcomes. Actually, I

Dennis McWilliams  15:04  
have a quick anecdote, if you don't mind. I mean, you know what you mentioned earlier about, you know, being able to identify good surgery versus bad. So we were looking at a company. And that was one of their value propositions that they could analyze. They could rate surgeon surgeon quality. So we did, we're doing our diligence. And we talked to a couple of health very large healthcare systems. And we I remember one call, we had the hospital CEO, and the Chief Medical Officer of the hospital system, both on the call, and we're kind of asking these questions about would this be important? And the CEO is like, yes, this would be super important to know, we would really love to know what surgeons need remedial training and those sorts of things. And then the CEO had to leave the call. And it was just me and the CEO, cmo left. And he's like, look, I don't want to contradict what my CEO said. But that's just not true. We do not necessarily want to know who our bad surgeons are, who are good ones are, like, blown away by this. This is like, okay, he says, he goes, Yeah, no, I mean, because oftentimes, our worst ones are our highest revenue generators. So we don't want to note this, like, it was, it was, you know, gets back into, you know, some of the challenges as you think about implementing this. And we were dealing this with SST with our blackbox. Like, it's, you know, the idea of having all this data coming in is unsettling for surgeons at times and for nursing staffs. And so it's a double edged sword that I think all of us are trying to sort through, I just thought that was just a great anecdote of the challenges here.

Lisa Carmel  16:28  
What do you what do you tell the

Dennis McWilliams  16:32  
we didn't give them that direct feedback. They may be here, I'm not gonna say anything.

Janani Reisenauer  16:41  
Well, I think I think you bring up a really good point, because in the surgical community, there is a little bit of fear of what does aI mean for my job? And what does aI mean for my decision making and what does aI mean for my judgment, and again, I categorize it as a virtual assistant, there's nobody that's going to take your hand and tell you what to do unless we talk about automation, which is an entirely different topic. But, but what's interesting to me about AI is that healthcare has become so big, it's become so siloed. And it's become really compartmentalized. So right now, if I'm a lung surgeon, and I'm seeing a patient with a pulmonary nodule, all I'm thinking about is the five year survival of that patient with that pulmonary nodule, and what's the efficacy and the outcomes of surgery versus sbrt versus ablation, what I'm not thinking about is that this person also has a family history of coronary artery disease, and elevated lipoprotein a level, and maybe they're creating clearance is terrible. And maybe what's ultimately going to put this person in the hospital is sepsis from a kidney infection, and it has nothing to do with their lung nodule. And it's a completely irrelevant conversation. And those are those holistic 60,000 foot view interpretations of AI that I think are really important that that I think will transform the way that we treat patients moving forward.

Brian Miller  17:57  
Yeah, I just want to add to the concept of a virtual assistant. And I think the approach at least now and and you know what, how we view it is really that human in the loop. Same as we've done with DaVinci, you're augmenting what the surgeon could do. And sometimes you're allowing them to do things that they have never been able to do, I think AI can help with that on the cognitive side. But having that human in the loop for many reasons, is, is the way to go. But

Dennis McWilliams  18:23  
but that is but the amount that that humans evolved is about, I mean, you look at some of the orthopedic robots, where I mean, literally, the physician interaction is pushing the green button at the end so they can get reimbursed. So I mean, I think, I mean, I agree if it were true. I'm sorry, I didn't know this, this is why they have the button because you certainly can't collect the payment on it. But I mean, I everywhere long way for autonomous surgery. And so but I mean, I think we are going to see, you know, this data in AI is going to push the limits of things, maybe some more mundane things that we can take out to reduce a lot of those errors. Now it'll be really interesting to see.

Phil Rackliffe  18:59  
The The only thing I'd add on on virtual assistant or needing help or physician in the room is the amount of companies now in the AR VR space, you know, we're aligned with a couple of them internally at GE HealthCare, but the ability to have a heads up display and being able to phone a friend and be able to see all your where your monitors are out in front of you on an AR headset and be able to hit a button and say called authorize an hour I'm in a tough spot uniport in to be able to see exactly what the doctor seeing to be able to say, Nope, go left, go right now use a different catheter. That's now and that's really exciting. Now wearing the headset is a whole different, you know, challenge to get through and payment of that, but the core technology itself, there's some amazing things going on right now as far as driving more efficiency, being able to have a friend available or another physician to help you through a surgery. Just boundless opportunities with that.

Janani Reisenauer  19:55  
It's a delicate balance because like I will tell you the reason why I want going into surgery is because the operating room is like the most calming, quiet, relaxing, like I'm the most relaxed when I'm in the operating room, which seems very paradoxical. But that's where I feel like I'm at my inner peace. And now all of a sudden, you've got these headsets and blaring monitors and data and all this other stuff telling you what to do. How do you how do you balance all of these streams of information that you're getting with just focusing on the task at hand, which is getting that patient through surgery, that will require some change management that will require training our surgeons differently, and sometimes more information is not always better? Sometimes it's remarkably better. And sometimes it's not, and filtering through that is something that we're going to have to tackle as we move forward.

Lisa Carmel  20:46  
So So you hear that you need a Zen setting, and a reimbursement button. Right. Okay. Everyone needs a green room solved all the problems there. No, okay. But yours. You were just saying on AR VR. There literally is 20. I don't know how many startups with goggles. Like, which, which ones are what, you know, weigh in here. Dennis?

Dennis McWilliams  21:15  
They could be sitting out here to look, I mean, it's one of the I mean, feels right. I mean, that the technology do a lot of these super cool things is here today. And it's not a technological hurdle on a lot of it. You know, I think for a lot of those technologies is who's gonna pay for it at the end of the day? Is the hospital going to come out of their global payment to cover the cost of this AR VR stuff? Like, is the surgeon going to pay out of pocket for the training, if you're going to use a virtual trainer for is the company get paid for it? These are all things that are having to get sorted out now. And I think it's problematic, but I, you know, the data overload thing is a huge problem. And I mean, this is it's interesting, I had a chance to my former life, we were doing some work with the military and like the military has dealt with us over the past 20 years in terms of massive data that they started to collect from battlefield like, you know, Battlefield. Data Management is a huge thing. And I think there are a lot of lessons we can learn from that. I'd be curious, I'm sure intuitive, and GE have like studied some of this and figuring out and I think that's kind of one of the nice things that AR VR can be used for is, you know, a better presentation of complex data in a multi dimensional way that it's just hard to do with traditional duty screens.

Brian Miller  22:23  
Now, it just your reference, the, you know, the military and, you know, kind of aviation and whatnot. And that that was a big parallel, when you started to look at what the surgeon console was, I mean, the surgery console for DaVinci. For those you don't know, it is an XR device itself, right, you can see the real endoscopic view, but you have that digital link where you can start to superimpose things on top of it. But it is one of those where you go okay, what information do they actually care? To see? When do they need to see it. And I actually think that's a lot harder of a task than figuring out, you know, with this piece of information be useful. If you show it at the wrong time, it is going to be useful at all. So I think there is a huge aspect, but there are a lot of parallels. For very complex, high risk, high pressure situations where it's you know, it's it's well studied.

Phil Rackliffe  23:09  
Yeah, the only thing I would say is the rate of change of AR VR technology scares me because it takes so long for a company like GE HealthCare to have whatever it's a HL one No, it's HL two, no, it's HL three. No, it's Magic Leap. No, it's Apple vision Pro. No, it's no, it's no, they're innovating at their clip, we can only catch up so fast. So by the time we have the product out, my worry is oh, well, there's something already better. Well, granted, there always be that, but that's tough. And so you know, we have a couple of partnerships with with one company that kind of oversees and or and gives you library of cases and they work very well. And then we have one, you know, with meta view that we talked about with omnify XR but yeah, I do worry about the rate of change of technology versus our ability to accept and incorporate the change.

Lisa Carmel  24:02  
We're we're sort of back talking about problems. But but but there's there's an opportunity here on data interoperability is an opportunity. Right? So what is going to help solve some of the problems on data in interoperability?

Brian Miller  24:22  
Yeah, I'll start there. Right. And this has been a long standing challenge. Interoperability, so it's not new. But I do think now you get to a point of going it's worth solving. Because if you can start to stitch things together, you know, I talked about earlier the power of being able to understand what happens in the operating room, but it would be great if you could also understand what the anesthesia machine did. It'd be great if you could understand exactly what all of the individuals in the room are doing. Because those have as big of an impact on the patient outcome as you know on what happens when the instruments are touching tissue and so so if you imagine the biggest opportunity is you just know everything that occurred. You can understand preoperatively what the you know, laying skipping dynamics where and then you understand what the outcomes. That's where you're going to get into this predictiveness. And so I think it's it's one of those where it's really hard to get interoperability, but I think it's critical now. And for me, it's worth solving because of the promise of what it can bring.

Janani Reisenauer  25:17  
Yeah, I think you're absolutely right to echo on top of that, I even take it back to, it doesn't even make sense to go to the ER, because now we've got so much information as to how this individual is going to behave from a healthcare standpoint, that it not only helps you do the procedure safer, but should you even be doing the procedure in the first place, because of this patient's comorbidities or their risk factors, or whatever else might be part of that background picture that we don't see as well right now, because just like, or blackbox, where you're getting multiple streams of data, it's the same in the preoperative chart. It's the same as you're predicting postoperative outcomes. There's multiple streams of data, and we still haven't figured out a great way to collate that and interpret it. And so I think there's a tremendous opportunity there to reshape how we make decisions, not only in the operating room, but should we be going to the operating room. And that I think, will drive aspects outside of data in AI, as we've talked about other med tech and other devices and alternative solutions and therapies.

Dennis McWilliams  26:22  
is one of the opportunities I think, for the startups. I mean, it's the fact that it is really hard if you're one of the large corporates to, to work on days, you're using your competitors products. I mean, let's face it, the collaborations don't work very well. And so we talk a lot about in our theses of this neutrality concept of, you know, there are certain companies we invest in that we know the acquirer cannot be, you know, you know, an intuitive or a GE or a j&j and Medtronic, because you instantly now make that company and their product unusable by all their competitors. And so there's a real role. And that's a big opportunity, because there's a lot of the tech companies who look at health care, being a third of GDP and thinking like, wow, we really want to be in that vertical. And so I think there's a real opportunity for those companies to be those continue that let's just call it net neutrality rule of surgical data that will allow this industry to really take off because if it continues to be proprietary platforms, and you've got each one of the large corporates, you know, has their own information system within the hospital, like this half of like having five epics in there, like that'll never work. And so these are some things we have to figure out. But it's also a massive opportunity for the startups.

Lisa Carmel  27:36  
Well, speaking of startups, we have a little bit of time here for each of you. I think there's startups here in the audience, and they might want to know, what you're looking for.

Dennis McWilliams  27:49  
To start with, I mean, I want to know what they're looking for.

Lisa Carmel  27:55  
Well, Phil's looking for everything clearly.

Phil Rackliffe  27:58  
Yeah, maybe I'll just start from a GE. You know, a lot of people are like, Oh, do you invest at a GE Capital? I mean, there's a lot of old thoughts of like how we used to invest? No, we're a separate company publicly traded as of a year ago, we invest from our balance sheet. We are very active. So it's it's round agnostic. So we've done A through F, various check sizes from two to 30. I think we have more favorable, I'll say terms and my friend, because what we're looking for, I'm not saying you have unfavorable terms that I'm sure your your terms are very fair, but no, you know, what, we are looking for less of a return, actually no return, we return yes, but we're not in it to make money. We're in it to find synergistic assets, that we can plug into our technology to enable long term growth. That's it. So as I'm sitting here, I love IR IO cardiopulmonary, I'll give my plugs right now, EP, all those are very interesting spaces for us of which we're quite active across. There's a number of other divisions within GE that are active as well. But we've made a lot of investments. And we will continue to do that. Because we believe that the best way to grow is through working with startups, because that's where the engine is going and bringing those in.

Brian Miller  29:26  
Yeah, I would mention two areas for us. One, when you start to look at some of the clinical decision making tools, you know, the ability to get as much information from the operative field as possible. So that it can be used along with you know, other pieces of information, but to help augment what is urgencies to make the decision. So there's a range of technologies in there but that that is of significant value, because I think there are opportunities we have some products now where surgeons are able to see things that are underneath the tissue couldn't see it in white light. So giving that information that does not exist. Just key. I think the other side going back to data for a moment, there's a recognition that across the continuum, there are key data sources that not one company is going to own and be able to drive and, and so there is a need to create this kind of the interoperability. But there's also looking out for a vast range of partnerships, to be able to really follow all the way from the patient first enters the door all the way till they're recovered, that includes at home where there's patient reported outcomes, things that really matter, as you start to understand what good surgery looks like. And so I think there's, as we talked about earlier, significant opportunity there for high quality, high availability, and then combined datasets to be able to really drive things.

Janani Reisenauer  30:47  
I have a slightly different perspective, given my background, but but I'm very fortunate to go into work every day and be surrounded by some of the best and brightest minds in medicine. And Mayo Clinic is the number one hospital in the world for health care for good reason, because of its employees. And I think the message to the startups is that is a wealth of clinical subject matter. Expert data, there's a wealth of knowledge there. And our PIs are investigators, our clinicians are willing to share that they're willing to co develop that because we want to be part of this healthcare revolution that reshapes how healthcare is defined, we don't want to just be the bystander, that's once the product has 510 K clearance and you're looking for somebody to purchase it. So you can tell somebody else, Mayo Clinic's using it. That's not what we really want to be, we want to be invested and feel like we helped shape the next generation of health care with you. So if you are a startup interested in a surgical intervention, and you're seeking a clinical subject matter expert, as a partner, we'd love to chat with you further.

Lisa Carmel  31:55  
But I think these guys need a quickstart guide, like, how do you really get? How do you start that conversation with the male? Yeah,

Janani Reisenauer  32:05  
that's hard. Because we're a big organization, just like just like the powerhouses that I'm sitting up next to on this stage. A great place to start is through our Mayo Clinic ventures group. We have some members from our ventures group here, and you're golden burgers here, somewhere, please here. And he's a great resource to understand how Mayo Clinic invests in companies. And near and I work very closely together, you can obviously reach out to me as well. And and we have to start the conversation somewhere to understand what it is you're looking for in a partner? And does that align with our areas of strategic priority? And is there an opportunity there to potentially either co develop, or serve as an early adopter of that technology by first inhuman work and in a transformative way across surgery?

Phil Rackliffe  32:52  
So Dennis, I would just say that, you know, deal flow of like, potential companies to invest in is is a lot, right, especially, it's a week to week now they're all flowing in look at Lulu, who helps me look through all these deals and tell tell us kind of what's good or not. You know, to me, it is through kind of a connection of a connection is one way another thing is like someone showing me that they cared, saying something to the tune of, Oh, listen, we know you're doing this type of AI procedure. In this type of specialty, we have something like do some homework before you just like, I'm looking for $5 million in a nav company, I don't care that that needs to be some kind of a hook to make you want to think about actually reading the rest of it. And I think that's really important too, just because there's a lot of potential investment opportunities right now, as

Dennis McWilliams  33:43  
we all know, well, after you just told him How soft your terms are expected. I didn't say

Lisa Carmel  33:50  
I don't know, Dennis.

Dennis McWilliams  33:55  
I, you know, like, I mean, we we've been active specifically in the space. I mean, obviously, we're looking for things that improve patient outcomes. We're looking for things that take costs out of the healthcare system, and we're looking to provide a return to our investors. So like, you know, all three of those names have to work for us, for it to work for Asante. And then there are other ways that we make that that equation all work. You know, our thesis within this specific space, though, we've broken it down into two broad categories, you know, specific, you know, kind of, you know, therapeutic technologies, decision support a lot of things that we've talked about today. We've also been really interested in what I would call picks and shovels I mean, the amount that dollars invested in AI far surpasses by an order of magnitude, the actual revenue generated by AI products and healthcare. And so that in itself is a mark. So we've made some investments in technologies that we think are enabling for the category, um, whose customers oftentimes you are the company is developing the algorithms. So we've we've done things on both sides of that. But you know, probably the biggest challenge we've run into kind of in this space is From a startup company, really what is the long term sustainable business model? Because we see a lot of things that are super interesting. And we see they're going to be the future of surgery. But at the end of the day, they're probably just a feature on one of their machines. And like, you know, how much is that really going to get from an exit standpoint, whereas, if you're going to open up a new category, if you can, like, move, you know, move significant new revenue streams, into their product lines, that's something that's gonna be a lot more compelling. And so those are the things we're trying to look for. And it's hard. Those are those are hard to find, because it's very hard. I mean, you've just heard how active to the largest medtech companies in the world are in the space. And we have to pick our battles on the things that we think a startup can do effectively, that that they can't and so it's a lot of lot of work.

Lisa Carmel  35:49  
Said, so you to co invest. How does that work?

Dennis McWilliams  35:56  
We've been very happy with them. So yeah, no, I mean, look at me, as you know, intuitive has intuitive as a, you know, a very good venture arm. And we were ironically, both looking at a similar deal. We had our thesis on perioperative data, they had already started working with intuitive into it intuitive at started a venture fund, and it was just a very natural thing for us to work on. And it's, it's interesting, because it wasn't series A ready. You know, it was something that we did a seed investment on. And it's been been a lot of work together. And what's been great about working with intuitive is one, it's not just the money, it's the really the partnership with Brian and the group just in terms of what they can provide in terms of know how knowledge, you know, that obviously helps the company relative to the things that they could do with intuitive, but also as enabling for other partnerships and other things they can do with other med tech companies. And that's what's been great about that partnership. And it's been, I think, from from both groups perspective of positive thing. Yeah,

Brian Miller  36:51  
yeah, I agree. And it gives us the opportunity to really look at things in early stages. It's been great, you

Lisa Carmel  36:57  
know, not to put you on the spot. But you now have a an an newish 100 and $50 million dollar fund. Is the investment thesis similar to the first one, it

Brian Miller  37:08  
is similar to the first one here, do you do you want to

Dennis McWilliams  37:12  
err your terms as good as?

Lisa Carmel  37:25  
Well, let's see. So Muriel is actually speaking, Denard McLean, who had spent years at intuitive and she'll be speaking on another panel, she can she can answer those questions.

Dennis McWilliams  37:38  
But I think all the court I mean, you know, I mean, all the corporate venture arms have been active in this space. I mean, you know, you know, every one of them has made, you know, both investments and acquisitions in the space. I mean, one of the largest acquisitions was digital surgery have ever Tronic. I mean, that was, I mean, to some extent, that drove a lot of the VC investment, when you see that level of commitment, we're like, wow, I mean, we really should pay attention that caught the attention of a lot of investors and entrepreneurs. So, I mean, every corporates active in the space, and I think, you know, they're, they're, you know, at different levels in terms of how well strategically that's been set out within their organization on a business unit basis. I mean, you did two world class examples here of that. I think other companies, their digital strategy, relative to their core business is evolving. But they're all active. I

Phil Rackliffe  38:23  
would just say the pedigree of and the history of the VC firm, that is in one of your earlier rounds, holds a lot of weight. And I sit at the different, you know, a different angle than what I was trying to raise money before. But if literally, and I'm not trying to, I'm trying to be nice to dentists, if if dentists are Asante is in around, absolutely looking. It was probably a list of like 20. And we're going to take a look and because I can already tell by that it has been pressure tested a lot, a lot of eyes, great diligence, very smart med tech people that have done very, very well. And so then we you know, so I would just, you know, be careful about it and be choosy and try to get the best you can and we'll follow on or lead frankly, now we do we do lead, we do lead and they're not that favorable terms. Let's say they're there. They're Reasonable. Reasonable, reasonable.

Lisa Carmel  39:16  
Yeah. And rumor has it honestly that Dennis is one of the nicest VCs out there.

Dennis McWilliams  39:25  
Now you're really hurting my reputation.

Lisa Carmel  39:31  
And on that note, I think we're we're out of time.

 

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